Data Analysis¶

In [1]:

import numpy as np
import hvplot.xarray
import holoviews as hv
from rasterio.enums import Resampling

from conflict_monitoring_ntl.case_studies import get_county_ids, get_date
from conflict_monitoring_ntl.viz import plot_tile_comparison, plot_binary
from conflict_monitoring_ntl.utils import get_gdf_for_admin
from conflict_monitoring_ntl.satellites import BlackMarble, SDGSat
from conflict_monitoring_ntl.transform import RasterPipeline

import numpy as np import hvplot.xarray import holoviews as hv from rasterio.enums import Resampling from conflict_monitoring_ntl.case_studies import get_county_ids, get_date from conflict_monitoring_ntl.viz import plot_tile_comparison, plot_binary from conflict_monitoring_ntl.utils import get_gdf_for_admin from conflict_monitoring_ntl.satellites import BlackMarble, SDGSat from conflict_monitoring_ntl.transform import RasterPipeline

In [2]:

import warnings
warnings.filterwarnings('ignore',
    message='Connection pool is full, discarding connection*',
    module='urllib3.connectionpool'
)

import warnings warnings.filterwarnings('ignore', message='Connection pool is full, discarding connection*', module='urllib3.connectionpool' )

In [3]:

import logging
logging.getLogger("pyogrio._io").setLevel(logging.WARNING)

import logging logging.getLogger("pyogrio._io").setLevel(logging.WARNING)

In [4]:

SDGSAT_THRESHOLDS = np.arange(1.0, 2.1, 0.1).tolist()
BM_THRESHOLDS = np.arange(0.5, 2.1, 0.1).tolist()

SDGSAT_THRESHOLDS = np.arange(1.0, 2.1, 0.1).tolist() BM_THRESHOLDS = np.arange(0.5, 2.1, 0.1).tolist()

In [5]:

rasters = [SDGSat(), BlackMarble()]

rasters = [SDGSat(), BlackMarble()]

Radiance Conversion and Band Scaling

• L denotes the radiance intensity at the entrance pupil of the sensor.
• L is measured in units of W/m²/sr/µm (watts per square meter per steradian per micrometer).
• DN represents the original digital number (pixel value) of the image.
• Gain is the amplification factor; Bias is the offset for radiometric calibration.

Radiance Calculation

$$ L = DN \times \text{Gain} + \text{Bias} $$

Conversion to nW/cm²/sr

To convert GLI band radiance to the brightness unit nW/cm²/sr(same as Black Marble), multiply by the bandwidth (in microns) and a SI scaling factor.

$$ \text{Radiance}_{(\text{nW}/\text{cm}^2/\text{sr})} = L_{(\text{W}/\text{m}^2/\text{sr}/\mu\text{m})} \times 10^5 \times \text{bandwidth}_{\mu\text{m}} $$

Sudan¶

In [6]:

country = "Sudan"
date, county_id = get_date(country), get_county_ids(country)[0]
county_gdf = get_gdf_for_admin(county_id)

transformations = [{"reproject_match": {"resampling": Resampling.bilinear}}, {}]
pipeline = RasterPipeline(county_gdf, date, rasters, transformations)
ds = pipeline.run()

country = "Sudan" date, county_id = get_date(country), get_county_ids(country)[0] county_gdf = get_gdf_for_admin(county_id) transformations = [{"reproject_match": {"resampling": Resampling.bilinear}}, {}] pipeline = RasterPipeline(county_gdf, date, rasters, transformations) ds = pipeline.run()

In [7]:

plot_tile_comparison(
    arr_left=ds.sdgsat_radiance,
    arr_right=ds.black_marble_radiance,
    title_left="SDGSat radiance (nW/cm²/sr)",
    title_right="Black Marble radiance (nW/cm²/sr)",
    clim_left=(7, 7.5),
    clim_right=(0, 2)
)

plot_tile_comparison( arr_left=ds.sdgsat_radiance, arr_right=ds.black_marble_radiance, title_left="SDGSat radiance (nW/cm²/sr)", title_right="Black Marble radiance (nW/cm²/sr)", clim_left=(7, 7.5), clim_right=(0, 2) )

Out[7]:

In [8]:

binary_dict_sdgsat = {t: plot_binary(ds.sdgsat_dn, "SDGSat", t) for t in SDGSAT_THRESHOLDS}
hv.HoloMap(binary_dict_sdgsat, kdims='threshold')

binary_dict_sdgsat = {t: plot_binary(ds.sdgsat_dn, "SDGSat", t) for t in SDGSAT_THRESHOLDS} hv.HoloMap(binary_dict_sdgsat, kdims='threshold')

Out[8]:

In [9]:

curve_dict_bm = {t: plot_binary(ds.black_marble_radiance, "Black Marble", t) for t in BM_THRESHOLDS}
hv.HoloMap(curve_dict_bm, kdims='threshold')

curve_dict_bm = {t: plot_binary(ds.black_marble_radiance, "Black Marble", t) for t in BM_THRESHOLDS} hv.HoloMap(curve_dict_bm, kdims='threshold')

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South Sudan¶

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